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vp-blog"><div class="blog-page-wrapper"><main id="main-content" class="vp-blog-main"><ul class="vp-article-type-wrapper"><li class="vp-article-type active"><a class="route-link" href="/article/">全部</a></li><li class="vp-article-type"><a class="route-link" href="/star/">星标</a></li></ul><div id="article-list" class="vp-article-list" role="feed"><div class="vp-article-wrapper"><article class="vp-article-item" vocab="https://schema.org/" typeof="Article"><!--[--><!--]--><!----><a class="route-link" href="/research/cv/diffusion.html"><header class="vp-article-title"><!----><!----><span property="headline">长篇解读扩散模型 | Stable Diffusion</span></header></a><div class="vp-article-excerpt"><h2>前言</h2>
<p>本文将解读目前最受欢迎的人工智能图像生成模型之一，稳定扩散模型（Stable Diffusion Models, SD）<sup class="footnote-ref"><a href="#footnote1">[1]</a><a class="footnote-anchor" id="footnote-ref1"></a></sup><a class="footnote-anchor" id="footnote-ref1">，该模型能够通过简单的文本描述生成出非常真实的描述图像。</a></p><a class="footnote-anchor" id="footnote-ref1">
<p>先前的扩散模型（Diffusion Model, DM）是将图像形成过程分解为去噪自编码器的连续应用（编码器-解码器），
并且允许一种指导机制来控制图像生成过程，而无需重新训练。<strong>但是需要大量的时间和GPU，推理成本高昂</strong></p>
<p>为了提高这类模型的可用性，同时减少其对资源的大量消耗，需要一种方法来降低训练和采样的计算复杂度。</p>
</a></div><hr class="vp-article-hr"><div class="page-info"><span class="page-author-info" aria-label="作者🖊" data-balloon-pos="down"><svg xmlns="http://www.w3.org/2000/svg" class="icon author-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="author icon"><path d="M649.6 633.6c86.4-48 147.2-144 147.2-249.6 0-160-128-288-288-288s-288 128-288 288c0 108.8 57.6 201.6 147.2 249.6-121.6 48-214.4 153.6-240 288-3.2 9.6 0 19.2 6.4 25.6 3.2 9.6 12.8 12.8 22.4 12.8h704c9.6 0 19.2-3.2 25.6-12.8 6.4-6.4 9.6-16 6.4-25.6-25.6-134.4-121.6-240-243.2-288z"></path></svg><span><a class="page-author-item" href="https://l1binn.github.io" target="_blank" rel="noopener noreferrer">高礼彬</a></span><span property="author" content="高礼彬"></span></span><!----><span class="page-date-info" aria-label="写作日期📅" data-balloon-pos="down"><svg xmlns="http://www.w3.org/2000/svg" class="icon calendar-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="calendar icon"><path d="M716.4 110.137c0-18.753-14.72-33.473-33.472-33.473-18.753 0-33.473 14.72-33.473 33.473v33.473h66.993v-33.473zm-334.87 0c0-18.753-14.72-33.473-33.473-33.473s-33.52 14.72-33.52 33.473v33.473h66.993v-33.473zm468.81 33.52H716.4v100.465c0 18.753-14.72 33.473-33.472 33.473a33.145 33.145 0 01-33.473-33.473V143.657H381.53v100.465c0 18.753-14.72 33.473-33.473 33.473a33.145 33.145 0 01-33.473-33.473V143.657H180.6A134.314 134.314 0 0046.66 277.595v535.756A134.314 134.314 0 00180.6 947.289h669.74a134.36 134.36 0 00133.94-133.938V277.595a134.314 134.314 0 00-133.94-133.938zm33.473 267.877H147.126a33.145 33.145 0 01-33.473-33.473c0-18.752 14.72-33.473 33.473-33.473h736.687c18.752 0 33.472 14.72 33.472 33.473a33.145 33.145 0 01-33.472 33.473z"></path></svg><span><!----></span><meta property="datePublished" content="2024-02-21T00:00:00.000Z"></span><!----><span class="page-reading-time-info" aria-label="阅读时间⌛" data-balloon-pos="down"><svg xmlns="http://www.w3.org/2000/svg" class="icon timer-icon" viewBox="0 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content="PT14M"></span><span class="page-category-info" aria-label="分类🌈" data-balloon-pos="down"><svg xmlns="http://www.w3.org/2000/svg" class="icon category-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="category icon"><path d="M148.41 106.992h282.176c22.263 0 40.31 18.048 40.31 40.31V429.48c0 22.263-18.047 40.31-40.31 40.31H148.41c-22.263 0-40.311-18.047-40.311-40.31V147.302c0-22.263 18.048-40.31 40.311-40.31zM147.556 553.478H429.73c22.263 0 40.311 18.048 40.311 40.31v282.176c0 22.263-18.048 40.312-40.31 40.312H147.555c-22.263 0-40.311-18.049-40.311-40.312V593.79c0-22.263 18.048-40.311 40.31-40.311zM593.927 106.992h282.176c22.263 0 40.31 18.048 40.31 40.31V429.48c0 22.263-18.047 40.31-40.31 40.31H593.927c-22.263 0-40.311-18.047-40.311-40.31V147.302c0-22.263 18.048-40.31 40.31-40.31zM730.22 920.502H623.926c-40.925 0-74.22-33.388-74.22-74.425V623.992c0-41.038 33.387-74.424 74.425-74.424h222.085c41.038 0 74.424 33.226 74.424 74.067v114.233c0 10.244-8.304 18.548-18.547 18.548s-18.548-8.304-18.548-18.548V623.635c0-20.388-16.746-36.974-37.33-36.974H624.13c-20.585 0-37.331 16.747-37.331 37.33v222.086c0 20.585 16.654 37.331 37.126 37.331H730.22c10.243 0 18.547 8.304 18.547 18.547 0 10.244-8.304 18.547-18.547 18.547z"></path></svg><!--[--><span class="page-category-item category0 clickable" role="navigation">深度学习</span><span class="page-category-item category4 clickable" role="navigation">计算机视觉</span><!--]--><meta property="articleSection" content="深度学习,计算机视觉"></span><span class="page-tag-info" aria-label="标签🏷" data-balloon-pos="down"><svg xmlns="http://www.w3.org/2000/svg" class="icon tag-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="tag icon"><path d="M939.902 458.563L910.17 144.567c-1.507-16.272-14.465-29.13-30.737-30.737L565.438 84.098h-.402c-3.215 0-5.726 1.005-7.634 2.913l-470.39 470.39a10.004 10.004 0 000 14.164l365.423 365.424c1.909 1.908 4.42 2.913 7.132 2.913s5.223-1.005 7.132-2.913l470.39-470.39c2.01-2.11 3.014-5.023 2.813-8.036zm-240.067-72.121c-35.458 0-64.286-28.828-64.286-64.286s28.828-64.285 64.286-64.285 64.286 28.828 64.286 64.285-28.829 64.286-64.286 64.286z"></path></svg><!--[--><span class="page-tag-item tag7 clickable" role="navigation">扩散模型</span><span class="page-tag-item tag4 clickable" role="navigation">diffusion model</span><span class="page-tag-item tag2 clickable" role="navigation">stable diffusion</span><!--]--><meta property="keywords" content="扩散模型,diffusion model,stable diffusion"></span></div></article></div><div class="vp-article-wrapper"><article class="vp-article-item" vocab="https://schema.org/" typeof="Article"><!--[--><!--]--><!----><a class="route-link" href="/research/cv/yolo-intro.html"><header class="vp-article-title"><!----><!----><span property="headline">长篇解读YOLO系列模型</span></header></a><div class="vp-article-excerpt"><p>目标检测的YOLO是一种基于单个神经网络的目标检测系统，由Joseph Redmon和Ali Farhadi等人于2015年提出。</p>
<p>YOLO将目标检测视为一个回归问题，直接预测输入图像中物体的类别和位置，与传统的两阶段目标检测算法（如R-CNN系列）不同，
YOLO不需要提前找到可能存在物体的区域，因此具有速度快、实时检测的优点。</p>
<h2><a class="header-anchor" href="#yolov1解读"><span>YOLOv1解读 <sup class="footnote-ref"></sup></span></a><a href="#footnote1">[1]</a><a class="footnote-anchor" id="footnote-ref1"></a></h2></div><hr class="vp-article-hr"><div class="page-info"><span class="page-author-info" aria-label="作者🖊" data-balloon-pos="down"><svg xmlns="http://www.w3.org/2000/svg" class="icon author-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="author icon"><path d="M649.6 633.6c86.4-48 147.2-144 147.2-249.6 0-160-128-288-288-288s-288 128-288 288c0 108.8 57.6 201.6 147.2 249.6-121.6 48-214.4 153.6-240 288-3.2 9.6 0 19.2 6.4 25.6 3.2 9.6 12.8 12.8 22.4 12.8h704c9.6 0 19.2-3.2 25.6-12.8 6.4-6.4 9.6-16 6.4-25.6-25.6-134.4-121.6-240-243.2-288z"></path></svg><span><a class="page-author-item" href="https://l1binn.github.io" target="_blank" rel="noopener noreferrer">高礼彬</a></span><span property="author" content="高礼彬"></span></span><!----><span class="page-date-info" aria-label="写作日期📅" 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